Seismic Data Quality Control Using Non-Linear Regression
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Solution Overview
Problem
Traditional seismic data quality control methods using linear regression analysis are insufficient in accurately distinguishing weak or noisy traces at near and far sensor offsets, leading to inappropriate rejection of valid data.
Innovation Solution
The technique employs non-linear regression analysis to determine the geophysical trend of trace amplitudes, allowing for more accurate filtering of seismic data by setting thresholds based on a non-linear model of logarithmic root mean square amplitude versus sensor offset, effectively accounting for both near and far offset data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If linear regression analysis is used for quality control, then the method is simple and easy to implement, but it inaccurately distinguishes weak or noisy traces at near and far sensor offsets
Solution Approach 1:
The patent transforms the linear regression model into a non-linear regression model by changing the mathematical parameters from linear to non-linear relationships. This allows the model to accurately capture the non-linear attenuation behavior of seismic waves at different offset distances, thereby improving measurement precision while maintaining computational feasibility through established non-linear regression algorithms.
2Device complexity
If linear regression is applied to seismic data, then computational complexity is low, but the geophysical trend determination is inaccurate for near and far offsets
Solution Approach 1:
The patent introduces non-linear curvature into the regression model to match the curved relationship between trace amplitude and sensor offset. By using non-linear functions (such as exponential or power law relationships) instead of straight lines, the model can properly fit the geophysical trend across the full range of offset distances, from near to far offsets, without requiring overly complex computational algorithms.
Data Source
AI summary
A technique includes receiving seismic data acquired in a seismic survey. The technique includes determining a geophysical trend of trace amplitudes indicated by the seismic data based on non-linear regression and performing quality control analysis on the seismic data based on the determined trend.